Development and <i>in vivo</i> testing of a high frequency endoscopic Raman spectroscopy system for potential applications in the detection of early colonic neoplasia
Bibliographic record
Abstract
The objective of this study was to build and test an adjunct system to a colonoscope for in vivo measurement of Raman spectra from colon tissue for potentially improving the detection of early cancers. The novelty of this system was that low cost fibre optic probes were used, without the addition of expensive optical filters. Good quality in vivo Raman spectra were successfully obtained with a 1 s integration time in the high frequency (HF) range from normal tissue and polyps of patients during a colonoscopy. The polyps were subsequently removed, and their pathology determined. The acquired in vivo Raman spectra showed clear changes between tissue with normal and tubular adenoma pathology. Further clinical study with this low cost HF Raman probe is warranted to fully test its clinical utility. Left: Raman probe orientated on a suspected polyp (indicated by arrow) under video surveillance during a colonoscopy. Right: average Raman spectra from 2800–3050 cm–1 obtained from polyps at different stages of disease. The peak intensities are in arbitrary units. magnified image Left: Raman probe orientated on a suspected polyp (indicated by arrow) under video surveillance during a colonoscopy. Right: average Raman spectra from 2800–3050 cm–1 obtained from polyps at different stages of disease. The peak intensities are in arbitrary units.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".